Sara Masiero | Artificial Intelligence | Outstanding Contributions in Academia Award

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Mrs. Sara Masiero | Artificial Intelligence | Outstanding Contributions in Academia Award

Collaboratrice at Scuola Universitaria Professionale della Svizzera Italiana, Switzerland

Sara Masiero is a dedicated and forward-thinking management engineer with a strong passion for innovation and digital transformation. She thrives on discovering new concepts and implementing solutions that enhance industrial efficiency, sustainability, and resilience. A firm believer in the power of serenity, she fosters an environment conducive to creativity and proactive engagement. Beyond her professional endeavors, Sara embraces adventure and cultural exploration, always seeking experiences that resonate with her positive energy.

Profile

Scopus

Education

Sara Masiero pursued her higher education at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI), where she obtained a Master of Science in Engineering (2018-2021). During her academic journey, she actively engaged in research projects focusing on optimizing industrial systems and integrating digital tools for process enhancement. Prior to her master’s degree, she earned a Bachelor of Science in Ingegneria Gestionale (2015-2018) from the same institution. She further honed her expertise through specialized programs, including the English Summer School at Horner School of English, AIGreen Business Lab by EIT Digital, and professional training in learning assessment methodologies.

Experience

Sara Masiero has amassed substantial experience in both academia and industry, contributing to projects that merge theoretical research with practical applications. Since November 2018, she has been serving as a scientific collaborator at SUPSI, where she plays a pivotal role in research and scientific development within the realm of Industry 4.0 and 5.0. Her work emphasizes human-centered industrial paradigms, sustainability, and resilience, while she also manages digital processes for EU H2020 projects and provides training in Industrial Engineering courses.

Between January 2023 and February 2024, Sara worked as a Business Process Manager at Masiero G. Srl and Z. Account Service Srl, overseeing financial and commercial processes related to sales, customer service, and supplier relations. She also ensured regulatory compliance and operational efficiency through effective bureaucratic and administrative process management. Earlier, she collaborated with STISA SA and LINNEA (September 2020 – February 2021) to develop her master’s thesis on optimizing material flows and warehouse layouts in logistics systems. Additionally, during her bachelor’s studies, she worked with RIRI SA (June 2018 – September 2018) on a thesis analyzing raw material purchasing processes with a focus on sustainability.

Research Interests

Sara Masiero’s research interests are deeply rooted in industrial innovation, digital transformation, and sustainability. She focuses on the integration of advanced digital tools in production systems, addressing the challenges and opportunities presented by Industry 4.0 and 5.0. Her work revolves around Quality Management advancements, human-centric industrial paradigms, and AI-driven digital platforms that enhance manufacturing processes. Furthermore, she explores methodologies for optimizing supply chain operations and ensuring regulatory compliance within rapidly evolving technological landscapes.

Awards and Recognition

Throughout her academic and professional journey, Sara has been recognized for her contributions to research and process optimization in industrial settings. Her innovative approach to digital transformation and industrial efficiency has earned her accolades in academic conferences and industry collaborations. She has actively participated in prestigious projects and workshops, further cementing her reputation as a knowledgeable and influential figure in the field of industrial engineering and management.

Publications

Corti, D., Masiero, S., & Gladysz, B. (2021). “Impact of Industry 4.0 on Quality Management: Identification of main challenges towards a Quality 4.0 approach.” IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), pp. 1-8.

Masiero, S., Qosaj, J., & Cutrona, V. (2024). “Digital Datasheet model: enhancing value of AI digital platforms.” Procedia Computer Science, 232, 149-158.

Masiero, S., Qosaj, J., Bettoni, A., & Gladysz, B. (2024). “Technology-Driven Measures for Human Centricity in the Manufacturing Sector.” International Association for the Management of Technology Conference, pp. 81-88, Cham: Springer Nature Switzerland.

Conclusion

Sara Masiero exemplifies the essence of a modern engineer—one who seamlessly integrates research, industry expertise, and a passion for innovation. Her extensive experience in digital transformation, quality management, and process optimization makes her a valuable contributor to the fields of industrial engineering and management. With a strong academic background, diverse professional experience, and a commitment to sustainability and human-centric methodologies, Sara continues to drive meaningful advancements in Industry 4.0 and 5.0. Her contributions to research and industry projects underscore her ability to bridge theoretical knowledge with practical applications, paving the way for smarter, more resilient production systems in the future.

Tushar Kafare | Artificial Intelligence | Best Researcher Award

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Dr. Tushar Kafare | Artificial Intelligence | Best Researcher Award

Assistant Professor at Sinhgad College of Engineering, India

Dr. Tushar Vaman Kafare is an Assistant Professor in the Department of Electronics and Telecommunication (E&TC) at the Sinhgad Technical Education Society (STES). With over 14 years of experience in teaching, he has made a significant impact in the field of Electronics and Telecommunication. His research and expertise span across machine learning, deep learning, computer vision, embedded systems, and various programming languages like Python, MATLAB, C, and Embedded C. Dr. Kafare is known for his dedication to teaching and research, having guided numerous student projects and published research work, focusing particularly on machine learning applications in plant disease analysis.

Profile

Google Scholar

Education

Dr. Kafare holds an M.E. degree in Electronics and Telecommunication, as well as a B.E. in Electronics. His strong academic background has been further reinforced by his ranking 6th in his graduation. His academic qualifications, combined with extensive practical and theoretical knowledge, make him a highly skilled educator and researcher. His ongoing Ph.D. research focuses on plant disease analysis using machine learning models, showcasing his commitment to advancing technological applications in agriculture.

Experience

Having joined STES on September 7, 2022, Dr. Kafare brings with him a wealth of experience in academia and industry. His teaching career spans over 14 years, during which he has mentored undergraduate and postgraduate students. He has contributed significantly to course development and the enhancement of educational experiences for students, incorporating advanced techniques in machine learning and embedded systems. Additionally, Dr. Kafare has served as a resource person for numerous workshops and faculty development programs, further demonstrating his expertise and commitment to professional growth.

Research Interests

Dr. Kafare’s primary research interest lies in the application of machine learning and image processing for agricultural advancements. His Ph.D. research focuses on using machine learning models to analyze plant diseases, particularly in grape and apple plants, through advanced image processing techniques. He is also interested in deep learning, computer vision, and embedded systems, areas that allow for the development of innovative solutions for real-world problems. Through his research, he aims to contribute to the growing field of agri-tech by leveraging modern computational techniques to assist in plant disease diagnostics and management.

Awards

Dr. Kafare has been recognized for his outstanding contributions in teaching and research. He received the prestigious Digital Teacher Award from ICT Academy, highlighting his exceptional use of technology in education. Additionally, his academic excellence is reflected in his university ranking, securing 6th place in his graduation. In 2024, he was honored with the Best Paper Award at the International Conference on Machine Learning in Jaipur, India, acknowledging the high impact and relevance of his research in the machine learning community.

Publications

Dr. Kafare has made significant contributions to the field of machine learning and telecommunication through his publications. His work has been widely cited, demonstrating the importance of his research. Below is a list of selected publications:

Kafare, T.V. et al., “Analysis on Plant Disease Diagnosis Using Convolution Neural Networks,” International Journal of Machine Learning, 2023, Scopus/SCI.

Kafare, T.V. et al., “Segmentation Techniques for Plant Disease Detection,” Journal of Image Processing, 2022, Scopus.

Kafare, T.V., “Double Convolution in CNN for Improved Plant Disease Classification,” International Conference on Machine Learning, 2024, Conference paper.

Kafare, T.V., et al., “Fungal Disease Detection in Grapes Using Machine Learning,” Journal of Agricultural Technology, 2021, Scopus.

Conclusion

Dr. Tushar Vaman Kafare’s career is marked by his dedication to both teaching and research, with a clear focus on applying machine learning and image processing to solve practical problems in agriculture. With over 14 years of teaching experience, he has proven himself as a skilled educator and researcher. His ongoing Ph.D. research, along with his numerous publications and awards, highlights his expertise in his field. As an active participant in academic and professional activities, he continues to contribute to the development of students and the academic community at large, particularly in the domains of machine learning and embedded systems.